{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XS-Wum5unJa5"
   },
   "source": [
    "# 1. 准备依赖环境"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "xroRbn53ckRV"
   },
   "outputs": [],
   "source": [
    "# !pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "executionInfo": {
     "elapsed": 8832,
     "status": "ok",
     "timestamp": 1646963128437,
     "user": {
      "displayName": "Kan Mammoth",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjzLXT03os5qw4O9nffWU8tUSsKcI_vt-Ny8euJ=s64",
      "userId": "03160031483950142290"
     },
     "user_tz": -480
    },
    "id": "dpffztqfb0nO"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import time\n",
    "import random\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from tqdm.notebook import tqdm_notebook\n",
    "from transformers import AdamW, BertTokenizer, BertForMaskedLM, BertModel\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from typing import Tuple, List\n",
    "from torch.nn.utils.rnn import pad_sequence\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_corpus(split, make_vocab=True, data_dir='../data/ThePeoplesDaily/'):\n",
    "    \"\"\"数据读取\n",
    "    \"\"\"\n",
    "\n",
    "    assert split.lower() in [\"train\", \"dev\", \"test\"]\n",
    "\n",
    "    word_lists = []\n",
    "    tag_lists = []\n",
    "    with open(os.path.join(data_dir, split + '.txt'), 'r', encoding='utf-8') as f:\n",
    "        word_list = []\n",
    "        tag_list = []\n",
    "        for line in f:\n",
    "            if line != '\\n':\n",
    "                word, tag = line.strip('\\n').split()\n",
    "                word_list.append(word)\n",
    "                tag_list.append(tag)\n",
    "            else:\n",
    "                word_lists.append(word_list)\n",
    "                tag_lists.append(tag_list)\n",
    "                word_list = []\n",
    "                tag_list = []\n",
    "    \n",
    "    if make_vocab:\n",
    "        word2id = build_map(word_lists)\n",
    "        tag2id = build_map(tag_lists)\n",
    "        return word_lists, tag_lists, word2id, tag2id\n",
    "    else:\n",
    "        return word_lists, tag_lists\n",
    "\n",
    "def build_map(lists):\n",
    "    maps = {}\n",
    "    for list_ in lists:\n",
    "        for e in list_:\n",
    "            if e not in maps:\n",
    "                maps[e] = len(maps)\n",
    "    return maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "286267\n",
      "23640982\n"
     ]
    }
   ],
   "source": [
    "train_word_lists, train_tag_lists, word2id, tag2id = build_corpus(\"train\")\n",
    "dev_word_lists, dev_tag_lists = build_corpus(\"dev\", make_vocab=False)\n",
    "test_word_lists, test_tag_lists = build_corpus(\"test\", make_vocab=False)\n",
    "\n",
    "char_sum = 0\n",
    "sentences = list()\n",
    "dataset = [train_word_lists, dev_word_lists, test_word_lists]\n",
    "for word_lists in dataset:\n",
    "    for word_list in word_lists:\n",
    "        sentences.append(''.join(word_list))\n",
    "        char_sum += len(''.join(word_list))\n",
    "        # print(''.join(word_list))\n",
    "\n",
    "print(len(sentences))\n",
    "print(char_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "深度调整产业结构,对于核心技术就有了更高的要求。以往的传统产业,尤其是粗放型的重工业,已经不适应目前发展的需要。应该从传统的生产型,转变为技术型。以我所在的首钢为例,首钢将要利用独有的冶炼技术,从生产型转变为服务型。老厂区停产后,其实也可以把原来厂区里的土地卖掉,开发房地产。不过,那样就不是首钢了。按照目前的规划,把老厂区转变为工业遗址,尤其是把高炉建成博物馆。这也就是在不放弃的基础上,继承传统,进而调整产业结构。\n"
     ]
    }
   ],
   "source": [
    "print(sentences[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YO8Phcprnw_K"
   },
   "source": [
    "# 2. 参数定义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "executionInfo": {
     "elapsed": 1223,
     "status": "ok",
     "timestamp": 1646963133110,
     "user": {
      "displayName": "Kan Mammoth",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjzLXT03os5qw4O9nffWU8tUSsKcI_vt-Ny8euJ=s64",
      "userId": "03160031483950142290"
     },
     "user_tz": -480
    },
    "id": "1GKrVcDYjGkk"
   },
   "outputs": [],
   "source": [
    "ask_and_answer_path = './data/ask_and_answer.json'\n",
    "download_model_path = '../bert_model/bert-pretrain/ThePeoplesDaily/' \n",
    "\n",
    "batch_size = 16\n",
    "epochs = 50\n",
    "max_length = 128\n",
    "seed = 900\n",
    "\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "torch.random.manual_seed(seed)\n",
    "torch.cuda.manual_seed(seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wO5jW6lHn2X-"
   },
   "source": [
    "# 3. Tokenizer & Bert-base-chinese"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217,
     "referenced_widgets": [
      "edf7a22f311649b69c0293243049571a",
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      "87079c0c072c457bb3c02d07a4dc2b1c",
      "119a72ab021b4ba5aa8f46c63aa0db31",
      "0e711ec996574dd3a75b3393ab9303b6",
      "2e23519c53aa4679b5bfa64cca63d0bc",
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      "2561b68a00c84d96a451f09b115531ec",
      "fe2d3687a8a44063a2266210fd6e831e"
     ]
    },
    "executionInfo": {
     "elapsed": 16692,
     "status": "ok",
     "timestamp": 1646894629837,
     "user": {
      "displayName": "Kan Mammoth",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjzLXT03os5qw4O9nffWU8tUSsKcI_vt-Ny8euJ=s64",
      "userId": "03160031483950142290"
     },
     "user_tz": -480
    },
    "id": "31OpzQAZjM_e",
    "outputId": "3f5a13a6-d66b-4a95-862c-9715a0db5687"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-chinese were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']\n",
      "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')\n",
    "model = BertForMaskedLM.from_pretrained('bert-base-chinese')\n",
    "# model = BertModel.from_pretrained('bert-base-chinese')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rmb8K_05jklm"
   },
   "source": [
    "# 4. 定义Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 868,
     "status": "ok",
     "timestamp": 1646895378163,
     "user": {
      "displayName": "Kan Mammoth",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjzLXT03os5qw4O9nffWU8tUSsKcI_vt-Ny8euJ=s64",
      "userId": "03160031483950142290"
     },
     "user_tz": -480
    },
    "id": "COn2lM2ljN-N",
    "outputId": "87cfd35f-868f-4740-deca-921a2ffe29a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "样本数量==>  input_ids   286267\n",
      "tensor([ 101,  782, 3696, 5381,  122, 3299,  122, 3189, 6380, 2945,  517, 5294,\n",
      "        5276, 3198, 2845,  518, 2845, 6887,  117, 5401, 1744, 1290, 2209, 6125,\n",
      "        5500, 2356, 1762, 8138, 2399, 4638, 3297, 1400,  671, 1921, 5326, 5330,\n",
      "         677, 3885,  117, 1469, 1059, 4413, 5500, 2356,  671, 3416,  117, 6963,\n",
      "         809, 3297, 7770, 5279, 2497, 2772, 2970, 6818, 3297, 7770, 5279, 2497,\n",
      "        5310, 3338, 3315, 2399, 4638,  769, 3211,  511,  102,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "           0,    0,    0,    0,    0,    0,    0,    0])\n"
     ]
    }
   ],
   "source": [
    "class LineByLineTextDataset(Dataset):  \n",
    "    def __init__(self):\n",
    "        # data_text = LineByLineTextDataset._read_ask_and_answer()\n",
    "        # print(data)\n",
    "        self.data_token = tokenizer(\n",
    "          text=sentences,\n",
    "          padding='max_length',\n",
    "          max_length=max_length,\n",
    "          truncation=True, #超过max_length长度的自动截断\n",
    "          return_tensors='pt'\n",
    "        )['input_ids']\n",
    "        # print(self.data_token)\n",
    "          \n",
    "    def __len__(self):\n",
    "        return len(self.data_token)\n",
    "  \n",
    "    def __getitem__(self, idx):\n",
    "        return self.data_token[idx]\n",
    "  \n",
    "    # @staticmethod\n",
    "    # def _read_ask_and_answer():\n",
    "    #     # json解析ask_and_answer文件\n",
    "    #     with open(ask_and_answer_path, 'r') as ask_and_answer:\n",
    "    #         records = json.load(ask_and_answer)['RECORDS']\n",
    "    #     # 读取ask_and_answer中的问题\n",
    "    #     data = list()\n",
    "    #     for record in records:\n",
    "    #         data.append(LineByLineTextDataset._drop_questionmark(record['title']))\n",
    "    #         for similar_ask in json.loads(record['similar_ask']):\n",
    "    #             if similar_ask['ask'] == '': continue\n",
    "    #             data.append(LineByLineTextDataset._drop_questionmark(similar_ask['ask']))\n",
    "    #     return data\n",
    "  \n",
    "    # @staticmethod\n",
    "    # def _drop_questionmark(text):\n",
    "    #     return text[:-1] if text[-1] == \"?\" or text[-1] == \"？\" else text\n",
    "    \n",
    "dataset = LineByLineTextDataset()\n",
    "print(\"样本数量==>  input_ids  \", dataset.__len__())\n",
    "print(dataset.__getitem__(idx=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0688SALFjsXm"
   },
   "source": [
    "# 5. 定义 DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "pOExvTlxjvt2"
   },
   "outputs": [],
   "source": [
    "dataloader = DataLoader(dataset, shuffle=True, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2qy1F7aOjyyN"
   },
   "source": [
    "# 6. Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "oTBOxkp4j0TG"
   },
   "outputs": [],
   "source": [
    "class Trainer:\n",
    "    def __init__(self, model, dataloader, tokenizer, mlm_probability=0.15, lr=1e-4, with_cuda=True, cuda_devices=None, log_freq=10):\n",
    "        self.device = torch.device(\"cuda:4\" if torch.cuda.is_available() else 'cpu')\n",
    "        self.model = model\n",
    "        self.is_parallel = False                        # 多GPU 数据并行？\n",
    "        self.dataloader = dataloader\n",
    "        self.tokenizer = tokenizer\n",
    "        self.mlm_probability = mlm_probability                 # masked的概率\n",
    "        self.log_freq = log_freq\n",
    "\n",
    "        # 多GPU训练\n",
    "    #     if with_cuda and torch.cuda.device_count() > 1:\n",
    "    #         print(f\"Using {torch.cuda.device_count()} GPUS for BERT\")\n",
    "    #         self.model = nn.DataParallel(self.model, device_ids=cuda_devices)\n",
    "    #         self.is_parallel = True\n",
    "        self.model.train()\n",
    "        self.model.to(self.device)\n",
    "        self.optim = AdamW(self.model.parameters(), lr=1e-4)\n",
    "        print(\"Total Parameters:\", sum([p.nelement() for p in self.model.parameters()]))\n",
    "      \n",
    "    def train(self, epochs):\n",
    "        for epoch in range(epochs):\n",
    "            ave_loss = self.iteration(epoch, self.dataloader)\n",
    "            if (epoch + 1) % 5 == 0:\n",
    "                model_path = download_model_path + f'Epoch{epoch + 1}_Batchsize{batch_size}_Loss{ave_loss:f}_DateTime{time.strftime(\"%Y%m%d_%H%M%S\", time.localtime())}/.'\n",
    "                model.save_pretrained(model_path)\n",
    "                print(f'Download into {model_path}')\n",
    "      \n",
    "    def iteration(self, epoch, dataloader, train=True):\n",
    "        str_code = 'Train'\n",
    "        total_loss = 0.0\n",
    "        with tqdm_notebook(total=len(dataloader), desc='Epoch %d Training' %epoch, ncols = 800) as pbar:\n",
    "            for i,batch in enumerate(dataloader):\n",
    "                # print(batch)\n",
    "                inputs, labels = self._mask_tokens(batch)\n",
    "                inputs = inputs.to(self.device)\n",
    "                labels = labels.to(self.device)\n",
    "                outputs = self.model(input_ids=inputs, labels=labels)\n",
    "                loss = outputs.loss.mean()\n",
    "\n",
    "                if train:\n",
    "                    self.model.zero_grad()\n",
    "                    self.optim.zero_grad()\n",
    "                    loss.backward()\n",
    "                    torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)\n",
    "                    self.optim.step()\n",
    "\n",
    "                total_loss += loss.item()\n",
    "                ave_loss = total_loss/(i+1)          \n",
    "                pbar.set_postfix(loss=float(ave_loss))\n",
    "                pbar.update(1)\n",
    "        return ave_loss        \n",
    "          \n",
    "    def _mask_tokens(self, inputs: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n",
    "        \"\"\" Masked Language Model \"\"\"\n",
    "        if self.tokenizer.mask_token is None:\n",
    "            raise ValueError(\n",
    "                \"This tokenizer does not have a mask token which is necessary for masked language modeling. Remove the --mlm flag if you want to use this tokenizer.\"\n",
    "            )\n",
    "        labels = inputs.clone()\n",
    "        # 使用mlm_probability填充张量\n",
    "        probability_matrix = torch.full(labels.shape, self.mlm_probability)\n",
    "        # 获取special token掩码\n",
    "        # Returns:  `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.\n",
    "        special_tokens_mask = [\n",
    "              self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()\n",
    "        ]\n",
    "        # 将special token位置的概率填充为0\n",
    "        probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0)\n",
    "        if self.tokenizer._pad_token is not None:\n",
    "          # padding掩码\n",
    "          padding_mask = labels.eq(tokenizer.pad_token_id)\n",
    "          # 将padding位置的概率填充为0\n",
    "          probability_matrix.masked_fill_(padding_mask, value=0.0)\n",
    "\n",
    "        # 对token进行mask采样\n",
    "        masked_indices = torch.bernoulli(probability_matrix).bool()\n",
    "        labels[~masked_indices] = -100 #loss只计算masked\n",
    "\n",
    "        # 80%的概率将masked token替换为[MASK]\n",
    "        indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices\n",
    "        inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)\n",
    "\n",
    "        # 10%的概率将masked token替换为随机单词\n",
    "        indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced\n",
    "        random_words = torch.randint(len(tokenizer), labels.shape, dtype=torch.long)\n",
    "        inputs[indices_random] = random_words[indices_random]\n",
    "\n",
    "        # 余下的10%不做改变\n",
    "        return inputs, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 324,
     "status": "ok",
     "timestamp": 1646896051848,
     "user": {
      "displayName": "Kan Mammoth",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjzLXT03os5qw4O9nffWU8tUSsKcI_vt-Ny8euJ=s64",
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     "text": [
      "Total Parameters: 102290312\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mammoth_kan/anaconda3/envs/custom_RasaX_env/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use thePyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n"
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    "trainer = Trainer(model, dataloader, tokenizer)"
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       "Epoch 0 Training:   0%|                                                                                       …"
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